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* Add TorchScript fork/join tutorial

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* Profiler recipe (pytorch#1019)

* Profiler recipe

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Adding a recipe for profiler

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make html-noplot

* [mobile] Mobile Perf Recipe

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* [Mobile Perf Recipe] Add the benchmarking part for iOS (pytorch#1055)

* [Mobile Perf Recipe] Add the benchmarking part for iOS

* [Mobile Perf Recipe] Add the benchmarking part for iOS

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* Update neural_style_tutorial.py (pytorch#1059)

Updated the mistake in the Loading Images Section.

* torch_script_custom_ops restructure (pytorch#1057)

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* Port custom ops tutorial to new registration API, increase testability.

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* Kill some other occurrences of RegisterOperators

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* Update README.md

* Make torch_script_custom_classes tutorial runnable

I also fixed some warnings in the tutorial, and fixed some minor bitrot
(e.g., torch::script::Module to torch::jit::Module)

I also added some missing quotes around some bash expansions.

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* Update torch_script_custom_classes to use TORCH_LIBRARY (pytorch#1062)

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* Tutorial for DDP + RPC (pytorch#1071)

* Update feature classification labels

* Update NVidia -> Nvidia

* Bring back default filename_pattern so that by default we run all galleries.

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* Tutorial for DDP + RPC.

Summary: Based on example from pytorch/examples#800

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* Make RPC profiling recipe into prototype tutorial (pytorch#1078)

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* Update feature classification labels

* Update NVidia -> Nvidia

* Bring back default filename_pattern so that by default we run all galleries.

Signed-off-by: Edward Z. Yang <ezyang@fb.com>

* Add prototype_source directory

* Add prototype directory

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* Remove extra "done"

* Add REAME.txt

* Update for prototype instructions

* Update for prototype feature

* refine torchvision_tutorial doc for windows

* Update neural_style_tutorial.py (pytorch#1059)

Updated the mistake in the Loading Images Section.

* torch_script_custom_ops restructure (pytorch#1057)

Signed-off-by: Edward Z. Yang <ezyang@fb.com>

* Port custom ops tutorial to new registration API, increase testability.

Signed-off-by: Edward Z. Yang <ezyang@fb.com>

* Kill some other occurrences of RegisterOperators

Signed-off-by: Edward Z. Yang <ezyang@fb.com>

* Update README.md

* Make torch_script_custom_classes tutorial runnable

I also fixed some warnings in the tutorial, and fixed some minor bitrot
(e.g., torch::script::Module to torch::jit::Module)

I also added some missing quotes around some bash expansions.

Signed-off-by: Edward Z. Yang <ezyang@fb.com>

* Update torch_script_custom_classes to use TORCH_LIBRARY (pytorch#1062)

Signed-off-by: Edward Z. Yang <ezyang@fb.com>

* Add Graph Mode Dynamic Quant tutorial

Summary:
Tutorial to demonstrate graph mode dynamic quant on BERT model.
Currently not directly runnable as it requires to download glue dataset and fine-tuned model

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* Add mobile recipes images

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* 1.6 model freezing tutorial (pytorch#1077)

* Update feature classification labels

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* Add prototype_source directory

* Add prototype directory

* Add prototype

* Remove extra "done"

* Add REAME.txt

* Update for prototype instructions

* Update for prototype feature

* refine torchvision_tutorial doc for windows

* Update neural_style_tutorial.py (pytorch#1059)

Updated the mistake in the Loading Images Section.

* torch_script_custom_ops restructure (pytorch#1057)

Signed-off-by: Edward Z. Yang <ezyang@fb.com>

* Port custom ops tutorial to new registration API, increase testability.

Signed-off-by: Edward Z. Yang <ezyang@fb.com>

* Kill some other occurrences of RegisterOperators

Signed-off-by: Edward Z. Yang <ezyang@fb.com>

* Update README.md

* Make torch_script_custom_classes tutorial runnable

I also fixed some warnings in the tutorial, and fixed some minor bitrot
(e.g., torch::script::Module to torch::jit::Module)

I also added some missing quotes around some bash expansions.

Signed-off-by: Edward Z. Yang <ezyang@fb.com>

* Update torch_script_custom_classes to use TORCH_LIBRARY (pytorch#1062)

Signed-off-by: Edward Z. Yang <ezyang@fb.com>

* Add Model Freezing in TorchScript

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* Update title

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* Update dcgan_faces_tutorial.py

Update labels to be floats to work around torch.full inference change.

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159 changes: 159 additions & 0 deletions advanced_source/rpc_ddp_tutorial.rst
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Combining Distributed DataParallel with Distributed RPC Framework
=================================================================
**Author**: `Pritam Damania <https://github.com/pritamdamania87>`_


This tutorial uses a simple example to demonstrate how you can combine
`DistributedDataParallel <https://pytorch.org/docs/stable/nn.html#torch.nn.parallel.DistributedDataParallel>`__ (DDP)
with the `Distributed RPC framework <https://pytorch.org/docs/master/rpc.html>`__
to combine distributed data parallelism with distributed model parallelism to
train a simple model. Source code of the example can be found `here <https://github.com/pytorch/examples/tree/master/distributed/rpc/ddp_rpc>`__.

Previous tutorials,
`Getting Started With Distributed Data Parallel <https://pytorch.org/tutorials/intermediate/ddp_tutorial.html>`__
and `Getting Started with Distributed RPC Framework <https://pytorch.org/tutorials/intermediate/rpc_tutorial.html>`__,
described how to perform distributed data parallel and distributed model
parallel training respectively. Although, there are several training paradigms
where you might want to combine these two techniques. For example:

1) If we have a model with a sparse part (large embedding table) and a dense
part (FC layers), we might want to put the embedding table on a parameter
server and replicate the FC layer across multiple trainers using `DistributedDataParallel <https://pytorch.org/docs/stable/nn.html#torch.nn.parallel.DistributedDataParallel>`__.
The `Distributed RPC framework <https://pytorch.org/docs/master/rpc.html>`__
can be used to perform embedding lookups on the parameter server.
2) Enable hybrid parallelism as described in the `PipeDream <https://arxiv.org/abs/1806.03377>`__ paper.
We can use the `Distributed RPC framework <https://pytorch.org/docs/master/rpc.html>`__
to pipeline stages of the model across multiple workers and replicate each
stage (if needed) using `DistributedDataParallel <https://pytorch.org/docs/stable/nn.html#torch.nn.parallel.DistributedDataParallel>`__.

|
In this tutorial we will cover case 1 mentioned above. We have a total of 4
workers in our setup as follows:


1) 1 Master, which is responsible for creating an embedding table
(nn.EmbeddingBag) on the parameter server. The master also drives the
training loop on the two trainers.
2) 1 Parameter Server, which basically holds the embedding table in memory and
responds to RPCs from the Master and Trainers.
3) 2 Trainers, which store an FC layer (nn.Linear) which is replicated amongst
themselves using `DistributedDataParallel <https://pytorch.org/docs/stable/nn.html#torch.nn.parallel.DistributedDataParallel>`__.
The trainers are also responsible for executing the forward pass, backward
pass and optimizer step.

|
The entire training process is executed as follows:

1) The master creates an embedding table on the Parameter Server and holds an
`RRef <https://pytorch.org/docs/master/rpc.html#rref>`__ to it.
2) The master, then kicks off the training loop on the trainers and passes the
embedding table RRef to the trainers.
3) The trainers create a ``HybridModel`` which first performs an embedding lookup
using the embedding table RRef provided by the master and then executes the
FC layer which is wrapped inside DDP.
4) The trainer executes the forward pass of the model and uses the loss to
execute the backward pass using `Distributed Autograd <https://pytorch.org/docs/master/rpc.html#distributed-autograd-framework>`__.
5) As part of the backward pass, the gradients for the FC layer are computed
first and synced to all trainers via allreduce in DDP.
6) Next, Distributed Autograd propagates the gradients to the parameter server,
where the gradients for the embedding table are updated.
7) Finally, the `Distributed Optimizer <https://pytorch.org/docs/master/rpc.html#module-torch.distributed.optim>`__ is used to update all the parameters.


.. attention::

You should always use `Distributed Autograd <https://pytorch.org/docs/master/rpc.html#distributed-autograd-framework>`__
for the backward pass if you're combining DDP and RPC.


Now, let's go through each part in detail. Firstly, we need to setup all of our
workers before we can perform any training. We create 4 processes such that
ranks 0 and 1 are our trainers, rank 2 is the master and rank 3 is the
parameter server.

We initialize the RPC framework on all 4 workers using the TCP init_method.
Once RPC initialization is done, the master creates an `EmbeddingBag <https://pytorch.org/docs/master/generated/torch.nn.EmbeddingBag.html>`__
on the Parameter Server using `rpc.remote <https://pytorch.org/docs/master/rpc.html#torch.distributed.rpc.remote>`__.
The master then loops through each trainer and kicks of the training loop by
calling ``_run_trainer`` on each trainer using `rpc_async <https://pytorch.org/docs/master/rpc.html#torch.distributed.rpc.rpc_async>`__.
Finally, the master waits for all training to finish before exiting.

The trainers first initialize a ``ProcessGroup`` for DDP with world_size=2
(for two trainers) using `init_process_group <https://pytorch.org/docs/stable/distributed.html#torch.distributed.init_process_group>`__.
Next, they initialize the RPC framework using the TCP init_method. Note that
the ports are different in RPC initialization and ProcessGroup initialization.
This is to avoid port conflicts between initialization of both frameworks.
Once the initialization is done, the trainers just wait for the ``_run_trainer``
RPC from the master.

The parameter server just initializes the RPC framework and waits for RPCs from
the trainers and master.


.. literalinclude:: ../advanced_source/rpc_ddp_tutorial/main.py
:language: py
:start-after: BEGIN run_worker
:end-before: END run_worker

Before we discuss details of the Trainer, let's introduce the ``HybridModel`` that
the trainer uses. As described below, the ``HybridModel`` is initialized using an
RRef to the embedding table (emb_rref) on the parameter server and the ``device``
to use for DDP. The initialization of the model wraps an
`nn.Linear <https://pytorch.org/docs/master/generated/torch.nn.Linear.html>`__
layer inside DDP to replicate and synchronize this layer across all trainers.

The forward method of the model is pretty straightforward. It performs an
embedding lookup on the parameter server using an
`RRef helper <https://pytorch.org/docs/master/rpc.html#torch.distributed.rpc.RRef.rpc_sync>`__
and passes its output onto the FC layer.


.. literalinclude:: ../advanced_source/rpc_ddp_tutorial/main.py
:language: py
:start-after: BEGIN hybrid_model
:end-before: END hybrid_model

Next, let's look at the setup on the Trainer. The trainer first creates the
``HybridModel`` described above using an RRef to the embedding table on the
parameter server and its own rank.

Now, we need to retrieve a list of RRefs to all the parameters that we would
like to optimize with `DistributedOptimizer <https://pytorch.org/docs/master/rpc.html#module-torch.distributed.optim>`__.
To retrieve the parameters for the embedding table from the parameter server,
we define a simple helper function ``_retrieve_embedding_parameters``, which
basically walks through all the parameters for the embedding table and returns
a list of RRefs. The trainer calls this method on the parameter server via RPC
to receive a list of RRefs to the desired parameters. Since the
DistributedOptimizer always takes a list of RRefs to parameters that need to
be optimized, we need to create RRefs even for the local parameters for our
FC layers. This is done by walking ``model.parameters()``, creating an RRef for
each parameter and appending it to a list. Note that ``model.parameters()`` only
returns local parameters and doesn't include ``emb_rref``.

Finally, we create our DistributedOptimizer using all the RRefs and define a
CrossEntropyLoss function.

.. literalinclude:: ../advanced_source/rpc_ddp_tutorial/main.py
:language: py
:start-after: BEGIN setup_trainer
:end-before: END setup_trainer

Now we're ready to introduce the main training loop that is run on each trainer.
``get_next_batch`` is just a helper function to generate random inputs and
targets for training. We run the training loop for multiple epochs and for each
batch:

1) Setup a `Distributed Autograd Context <https://pytorch.org/docs/master/rpc.html#torch.distributed.autograd.context>`__
for Distributed Autograd.
2) Run the forward pass of the model and retrieve its output.
3) Compute the loss based on our outputs and targets using the loss function.
4) Use Distributed Autograd to execute a distributed backward pass using the loss.
5) Finally, run a Distributed Optimizer step to optimize all the parameters.

.. literalinclude:: ../advanced_source/rpc_ddp_tutorial/main.py
:language: py
:start-after: BEGIN run_trainer
:end-before: END run_trainer
.. code:: python
Source code for the entire example can be found `here <https://github.com/pytorch/examples/tree/master/distributed/rpc/ddp_rpc>`__.
191 changes: 191 additions & 0 deletions advanced_source/rpc_ddp_tutorial/main.py
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import os
from functools import wraps

import random
import torch
import torch.distributed as dist
import torch.distributed.autograd as dist_autograd
import torch.distributed.rpc as rpc
from torch.distributed.rpc import ProcessGroupRpcBackendOptions
import torch.multiprocessing as mp
import torch.optim as optim
from torch.distributed.optim import DistributedOptimizer
from torch.distributed.rpc import RRef
from torch.nn.parallel import DistributedDataParallel as DDP

NUM_EMBEDDINGS = 100
EMBEDDING_DIM = 16

# BEGIN hybrid_model
class HybridModel(torch.nn.Module):
r"""
The model consists of a sparse part and a dense part. The dense part is an
nn.Linear module that is replicated across all trainers using
DistributedDataParallel. The sparse part is an nn.EmbeddingBag that is
stored on the parameter server.
The model holds a Remote Reference to the embedding table on the parameter
server.
"""

def __init__(self, emb_rref, device):
super(HybridModel, self).__init__()
self.emb_rref = emb_rref
self.fc = DDP(torch.nn.Linear(16, 8).cuda(device), device_ids=[device])
self.device = device

def forward(self, indices, offsets):
emb_lookup = self.emb_rref.rpc_sync().forward(indices, offsets)
return self.fc(emb_lookup.cuda(self.device))
# END hybrid_model

# BEGIN setup_trainer
def _retrieve_embedding_parameters(emb_rref):
param_rrefs = []
for param in emb_rref.local_value().parameters():
param_rrefs.append(RRef(param))
return param_rrefs


def _run_trainer(emb_rref, rank):
r"""
Each trainer runs a forward pass which involves an embedding lookup on the
parameter server and running nn.Linear locally. During the backward pass,
DDP is responsible for aggregating the gradients for the dense part
(nn.Linear) and distributed autograd ensures gradients updates are
propagated to the parameter server.
"""

# Setup the model.
model = HybridModel(emb_rref, rank)

# Retrieve all model parameters as rrefs for DistributedOptimizer.

# Retrieve parameters for embedding table.
model_parameter_rrefs = rpc.rpc_sync(
"ps", _retrieve_embedding_parameters, args=(emb_rref,))

# model.parameters() only includes local parameters.
for param in model.parameters():
model_parameter_rrefs.append(RRef(param))

# Setup distributed optimizer
opt = DistributedOptimizer(
optim.SGD,
model_parameter_rrefs,
lr=0.05,
)

criterion = torch.nn.CrossEntropyLoss()
# END setup_trainer

# BEGIN run_trainer
def get_next_batch(rank):
for _ in range(10):
num_indices = random.randint(20, 50)
indices = torch.LongTensor(num_indices).random_(0, NUM_EMBEDDINGS)

# Generate offsets.
offsets = []
start = 0
batch_size = 0
while start < num_indices:
offsets.append(start)
start += random.randint(1, 10)
batch_size += 1

offsets_tensor = torch.LongTensor(offsets)
target = torch.LongTensor(batch_size).random_(8).cuda(rank)
yield indices, offsets_tensor, target

# Train for 100 epochs
for epoch in range(100):
# create distributed autograd context
for indices, offsets, target in get_next_batch(rank):
with dist_autograd.context() as context_id:
output = model(indices, offsets)
loss = criterion(output, target)

# Run distributed backward pass
dist_autograd.backward(context_id, [loss])

# Tun distributed optimizer
opt.step(context_id)

# Not necessary to zero grads as each iteration creates a different
# distributed autograd context which hosts different grads
print("Training done for epoch {}".format(epoch))
# END run_trainer


# BEGIN run_worker
def run_worker(rank, world_size):
r"""
A wrapper function that initializes RPC, calls the function, and shuts down
RPC.
"""
os.environ['MASTER_ADDR'] = 'localhost'
os.environ['MASTER_PORT'] = '29500'


rpc_backend_options = ProcessGroupRpcBackendOptions()
rpc_backend_options.init_method='tcp://localhost:29501'

# Rank 2 is master, 3 is ps and 0 and 1 are trainers.
if rank == 2:
rpc.init_rpc(
"master",
rank=rank,
world_size=world_size,
rpc_backend_options=rpc_backend_options)

# Build the embedding table on the ps.
emb_rref = rpc.remote(
"ps",
torch.nn.EmbeddingBag,
args=(NUM_EMBEDDINGS, EMBEDDING_DIM),
kwargs={"mode": "sum"})

# Run the training loop on trainers.
futs = []
for trainer_rank in [0, 1]:
trainer_name = "trainer{}".format(trainer_rank)
fut = rpc.rpc_async(
trainer_name, _run_trainer, args=(emb_rref, rank))
futs.append(fut)

# Wait for all training to finish.
for fut in futs:
fut.wait()
elif rank <= 1:
# Initialize process group for Distributed DataParallel on trainers.
dist.init_process_group(
backend="gloo", rank=rank, world_size=2)

# Initialize RPC.
trainer_name = "trainer{}".format(rank)
rpc.init_rpc(
trainer_name,
rank=rank,
world_size=world_size,
rpc_backend_options=rpc_backend_options)

# Trainer just waits for RPCs from master.
else:
rpc.init_rpc(
"ps",
rank=rank,
world_size=world_size,
rpc_backend_options=rpc_backend_options)
# parameter server do nothing
pass

# block until all rpcs finish
rpc.shutdown()


if __name__=="__main__":
# 2 trainers, 1 parameter server, 1 master.
world_size = 4
mp.spawn(run_worker, args=(world_size, ), nprocs=world_size, join=True)
# END run_worker
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